CN114781435A - Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimized variation modal decomposition - Google Patents
Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimized variation modal decomposition Download PDFInfo
- Publication number
- CN114781435A CN114781435A CN202210300673.3A CN202210300673A CN114781435A CN 114781435 A CN114781435 A CN 114781435A CN 202210300673 A CN202210300673 A CN 202210300673A CN 114781435 A CN114781435 A CN 114781435A
- Authority
- CN
- China
- Prior art keywords
- electronic circuit
- power electronic
- optimization algorithm
- decomposition
- modal decomposition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 61
- 238000005457 optimization Methods 0.000 title claims abstract description 48
- 238000003745 diagnosis Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000013528 artificial neural network Methods 0.000 claims abstract description 15
- 241000272184 Falconiformes Species 0.000 claims description 43
- 230000006870 function Effects 0.000 claims description 17
- 238000004088 simulation Methods 0.000 claims description 10
- 230000015556 catabolic process Effects 0.000 claims description 9
- 238000006731 degradation reaction Methods 0.000 claims description 9
- 239000003990 capacitor Substances 0.000 claims description 8
- 230000000739 chaotic effect Effects 0.000 claims description 6
- 230000014509 gene expression Effects 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000005315 distribution function Methods 0.000 claims description 4
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000000750 progressive effect Effects 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims description 2
- 238000009827 uniform distribution Methods 0.000 claims description 2
- 230000005484 gravity Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 2
- 230000003993 interaction Effects 0.000 abstract description 2
- 239000000284 extract Substances 0.000 abstract 1
- 238000005070 sampling Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000019637 foraging behavior Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2832—Specific tests of electronic circuits not provided for elsewhere
- G01R31/2836—Fault-finding or characterising
- G01R31/2846—Fault-finding or characterising using hard- or software simulation or using knowledge-based systems, e.g. expert systems, artificial intelligence or interactive algorithms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/32—Circuit design at the digital level
- G06F30/33—Design verification, e.g. functional simulation or model checking
- G06F30/3308—Design verification, e.g. functional simulation or model checking using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Computer Hardware Design (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Geometry (AREA)
- Medical Informatics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimized variation modal decomposition. And decomposing the voltage signal by the optimized variation modal decomposition, selecting an effective modal component by using a Cramer correlation coefficient to reconstruct the signal, extracting the characteristics of a time domain and a frequency domain, and constructing a probabilistic neural network as a classifier for fault diagnosis of the power electronic circuit. The method solves the problem that the interaction between the decomposition layer number K of the variation modal decomposition and the penalty factor alpha influences the decomposition effect, selects an effective modal component reconstruction signal by using the Cramer correlation coefficient, extracts time domain and frequency domain characteristics, ensures the comprehensiveness of the extracted characteristics, and improves the diagnosis rate of faults.
Description
Technical Field
The invention relates to the technical field of power electronic circuit fault diagnosis, in particular to a power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variational modal decomposition.
Background
The power electronic technology is taken as a basic science of comprehensive application technology and is widely applied to the fields of national defense and military, industrial production, renewable energy systems and the like. Modern electronic systems are getting larger and larger in scale, more and more complex in structure and more changeable in working environment, the probability of system failure is increased, and the reliability and safety of power electronic circuits serving as key components of the electronic systems are affected by internal devices and external environments, so that the key problems are solved. With the development of theoretical research and practical application of machine learning and deep learning, a new research idea is provided for fault diagnosis of power electronic circuits.
Therefore, the invention provides a power electronic circuit fault diagnosis method based on the optimized variational modal decomposition of the improved Harris eagle optimization algorithm. Harris Hawk Optimization (HHO) has a inspiration derived from cooperative foraging behavior of Harris hawks, and models the process of global search and enclosure to simulate the complex process of Harris hawks enclosing the game under real conditions. HHO algorithms have been widely applied to the solution of various optimization problems due to their simple principles, less parameter adjustments, and strong stability. However, the HHO algorithm has the same problems of convergence precision, local optimization and the like as many group intelligent optimization algorithms. The Variable Mode Decomposition (VMD) is widely applied to feature extraction of fault signals, but does not consider the influence of the interaction between the decomposition layer number K of the VMD and the penalty factor α on the decomposition effect.
Disclosure of Invention
The invention aims to provide a power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimized variation modal decomposition, and provides an HHO algorithm based on Circle chaotic mapping and Cauchy differential variation operation aiming at the defects of the HHO algorithm. Decomposing the acquired voltage fault signals by the optimized VMD, effectively selecting modal components by using a Cramer correlation coefficient, reconstructing the signals, extracting 10-dimensional characteristics of a time domain and a frequency domain to form a characteristic vector, and constructing a probabilistic neural network as a classifier for fault diagnosis of the power electronic circuit.
In order to achieve the above purpose, the solution of the invention is:
the power electronic circuit fault diagnosis method based on the optimized variational modal decomposition of the improved Harris eagle optimization algorithm is characterized by comprising the following steps (1) to (5):
1. a power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition comprises the following steps:
step (1): establishing a simulation model of an actual electronic circuit based on a Simulink simulation platform in Matlab, selecting input voltage and output voltage signals of an electrolytic capacitor under different degradation degrees as data sets, and classifying the corresponding degradation degrees as fault types;
step (2): for the voltage signals collected in the step (1), simultaneously optimizing the parameter decomposition layer number K and the penalty factor alpha of variational modal decomposition by using an improved Harris eagle optimization algorithm, and minimizing the envelopment Shannon entropy into a fitness function of the Harris eagle optimization algorithm; obtaining a plurality of intrinsic mode functions after carrying out variation modal decomposition on the acquired voltage signals, calculating an enveloping Shannon entropy value of each intrinsic mode function, wherein parameters K and alpha corresponding to the minimum enveloping Shannon entropy value are optimal parameters of the variation modal decomposition;
and (3): processing the voltage signal by using the optimized variation modal decomposition, calculating a Cramer correlation coefficient between the intrinsic modal component and the original signal, selecting the intrinsic modal component larger than a threshold value, reconstructing the signal and extracting 10-dimensional characteristics of the time domain and the frequency domain to form a characteristic vector; wherein, the 10-dimensional characteristics are respectively the maximum value, the minimum value, the mean value, the square root amplitude, the variance, the standard deviation, the barycentric frequency, the kurtosis, the frequency variance and the mean square frequency of the signal;
and (4): constructing a probabilistic neural network diagnosis model, taking the characteristic vector in the step (3) as the input of the probabilistic neural network, taking the corresponding fault category as the output of the probabilistic neural network, training the probabilistic neural network, and taking the trained model as a power electronic circuit fault diagnosis model;
and (5): and (4) inputting the feature vector acquired in the step (3) into the trained probabilistic neural network model in the step (4) for the voltage signal acquired in the actual power electronic circuit, and obtaining the fault type of the actual power electronic circuit according to the output of the power electronic circuit fault diagnosis model.
2. The method for diagnosing the fault of the power electronic circuit based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm according to claim 1, wherein the voltage signal acquired by the simulation model in the step (1) is set as an input voltage signal and an output voltage signal when the nominal value of the electrolytic capacitor in the power electronic circuit is sequentially degraded by 2%.
3. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm according to claim 1, wherein the specific steps of optimizing the variational modal decomposition based on the improved harris eagle optimization algorithm in the step (2) are as follows:
step 3.1: the parameters are set as follows: initializing a population position vector of Harris hawk as [ K, alpha ], the population scale is N, the maximum iteration frequency is T, the upper and lower boundaries are LB and UB respectively, and the dimensionality of an objective function is D;
step 3.2: introducing Circle chaotic mapping to form an initialization population with uniform distribution, and simultaneously recording the current optimal individual and position;
step 3.3: processing the acquired voltage signals by using variational modal decomposition according to the position of each Harris hawk, and calculating the envelope Shannon entropy corresponding to each Harris hawk individual;
step 3.4: updating the escape energy E and the jump strength J, selecting four strategies according to the escape energy E and the escape probability r, and updating the optimal individual and position;
step 3.5: performing Cauchy differential variation operation on the current individual, the optimal individual and the randomly selected individual, and calculating and updating the current optimal individual and the position;
step 3.6: and when the constraint condition of the maximum iteration times is met, outputting an optimal parameter combination [ K, alpha ], otherwise, returning to the step 3.3.
4. The power electronic circuit fault diagnosis method for optimizing variable modal decomposition based on the improved harris eagle optimization algorithm according to claim 1, wherein the fitness function of the optimized variable modal decomposition of the harris eagle optimization algorithm in the step (2) is represented as follows:
Fitness＝MESE＝min{IMFESE(1),…,IMFESE(k)}
wherein the content of the first and second substances,
bithe envelope amplitude of the ith modal signal after the variation modal decomposition is obtained, M is the length of the modal signal, piFor normalizing the envelope, IMF, of the modal signalESE(k) Is the shannon entropy value of the envelope of the k-th mode signal.
5. The method for diagnosing faults of a power electronic circuit based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm as claimed in claim 1, wherein the formula of the gram correlation coefficient in the step (3) is as follows:
wherein phi iscThe method is characterized in that the method is a Cramer correlation coefficient, Z is an intrinsic mode component after variable mode decomposition, Z is an original signal, N is a sample size related to testing, and N is a small category number of any variable.
6. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm as claimed in claim 1, wherein the threshold value in the step (3) is set to 0.6.
7. The power electronic circuit fault diagnosis method based on the improved harris eagle optimization algorithm optimized variation modal decomposition of claim 3, wherein the formula of the Ciecle chaotic mapping in the step 3.2 is as follows:
where mod is a remainder function, a and b are coefficients, taken as 0.6 and 0.3, respectively.
8. The method for diagnosing the fault of the power electronic circuit based on the optimized variational modal decomposition of the improved harris eagle optimization algorithm according to the claim 3, wherein the four strategy expressions in the step 3.4 are as follows:
the first strategy is as follows: when E is more than or equal to 0.5 and r is more than or equal to 0.5, a soft enclosure strategy is adopted, and the formula is as follows:
X(t+1)＝△X(t)-E|JXrabbit(t)-X(t)|
wherein, Δ X (t) ═ Xrabbit(t)-X(t)，J＝2(1-r5)，r5Is [0,1 ]]A random number in between, and a random number,
E0is [ -1,1 [ ]]T is the current iteration number;
and (2) strategy two: when | E | <0.5 and r ≧ 0.5, a hard bounding strategy is adopted, and the formula is as follows:
X(t+1)＝Xrabbit(t)-E|△X(t)|
strategy three: when E is more than or equal to 0.5 and r is less than 0.5, adopting a gradual fast dive soft enclosure strategy, wherein the formula is as follows:
d is a problem dimension, S is a random vector of the D dimension, and LF is a Levy flight function;
and (4) strategy four: when | E | <0.5 and r <0.5, a hard surrounding strategy of progressive fast dive is adopted, and the formula is as follows:
wherein the content of the first and second substances,
Xmand N is the average position of the current population.
9. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm as claimed in claim 3, wherein the mathematical expression of the cauchy differential variation in the step 3.5 is as follows:
X(t+1)＝w1·f1·(X*-X(t))+w2·f2·(Xrand-X(t))
wherein, w1And w2Is a weight coefficient; f. of1And f2Taking a standard Cauchy distribution function with a mean value of 0 and a variance of 1 as a coefficient of the Cauchy distribution function; x*For the current optimal individual position, XrandFor a randomly selected position vector of harris hawks, x (t) is the individual position of the current harris hawk.
Drawings
Fig. 1 is a flow chart of a power electronic circuit fault diagnosis method based on an improved harris eagle optimization algorithm to optimize variational modal decomposition according to an embodiment of the present invention;
fig. 2 is a power electronic circuit simulation topology diagram according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings as follows:
the invention provides a power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimized variation modal decomposition, which has the general idea that:
firstly, a Simulink simulation model of an actual power electronic circuit is established, and input voltage signals and output voltage signals of an electrolytic capacitor under different degradation degrees are selected as original data sets, wherein the corresponding degradation degree is a fault type. The method is input into variational modal decomposition optimized by a Harris eagle optimization algorithm, and provides an HHO algorithm based on Circle chaotic mapping and Cauchy differential variation operation aiming at the problems of convergence precision and local optimization of the Harris eagle optimization algorithm. Decomposing the acquired voltage fault signal by the optimized VMD, effectively selecting modal components by using a Cramer correlation coefficient, reconstructing the signal, extracting 10-dimensional characteristics of a time domain and a frequency domain to form a characteristic vector, and constructing a probabilistic neural network as a classifier for fault diagnosis of the power electronic circuit.
As shown in fig. 1, the specific implementation of the power electronic circuit fault diagnosis method based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm of the present invention includes the following steps (1) to (5):
step (1): establishing a simulation model of an actual electronic circuit based on a Simulink simulation platform in Matlab, selecting input voltage and output voltage signals of an electrolytic capacitor under different degradation degrees as data sets, and classifying the corresponding degradation degrees as fault types;
step (2): for the voltage signals collected in the step (1), simultaneously optimizing the parameter decomposition layer number K and the penalty factor alpha of the variational modal decomposition by using an improved Harris eagle optimization algorithm, and minimizing the envelope Shannon entropy into a fitness function of the Harris eagle optimization algorithm; obtaining a plurality of intrinsic mode functions after carrying out variation modal decomposition on the acquired voltage signals, calculating an enveloping Shannon entropy value of each intrinsic mode function, wherein parameters K and alpha corresponding to the minimum enveloping Shannon entropy value are optimal parameters of the variation modal decomposition;
and (3): processing the voltage signal by using the optimized variation modal decomposition, calculating a Cramer correlation coefficient between the intrinsic modal component and the original signal, selecting the intrinsic modal component larger than a threshold value, reconstructing the signal and extracting 10-dimensional characteristics of the time domain and the frequency domain of the signal to form a characteristic vector; wherein, the 10-dimensional characteristics are respectively the maximum value, the minimum value, the mean value, the square root amplitude, the variance, the standard deviation, the barycentric frequency, the kurtosis, the frequency variance and the mean square frequency of the signal;
and (4): constructing a probabilistic neural network diagnosis model, taking the characteristic vector in the step (3) as the input of the probabilistic neural network, taking the corresponding fault category as the output of the probabilistic neural network, training the probabilistic neural network, and taking the trained model as a power electronic circuit fault diagnosis model;
and (5): and (3) inputting the voltage signals collected in the actual power electronic circuit into the trained probabilistic neural network model in the step (4) according to the characteristic vector obtained by the method in the step (3), and obtaining the fault type of the actual power electronic circuit according to the output of the power electronic circuit fault diagnosis model.
In this embodiment, the parameters of the electrolytic capacitor in the step (1) under different degradation degrees and the corresponding failure modes are set as shown in table 1 below;
TABLE 1 electrolytic capacitor parameters and corresponding failure modes
In this embodiment, 10-dimensional feature expressions such as the maximum value, the minimum value, and the mean value of the extracted signal in the step (3) are shown in table 2 below;
table 210 dimensional Signal feature expressions
Wherein x isiFor the signal sequence, i is 1,2, …, n, n is the number of sampling points, f is the sampling frequency, and p (f) is the power of each frequency in the spectrum obtained by fourier transform at the sampling frequency f.
The above embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical solution according to the technical idea of the present invention fall within the protective scope of the present invention.
Claims (9)
1. A power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition is characterized by comprising the following steps of:
step (1): establishing a simulation model of an actual electronic circuit based on a Simulink simulation platform in Matlab, selecting input voltage and output voltage signals of an electrolytic capacitor under different degradation degrees as data sets, and classifying the corresponding degradation degrees as fault types;
step (2): for the voltage signals collected in the step (1), simultaneously optimizing the parameter decomposition layer number K and the penalty factor alpha of variational modal decomposition by using an improved Harris eagle optimization algorithm, and minimizing the envelopment Shannon entropy into a fitness function of the Harris eagle optimization algorithm; obtaining a plurality of intrinsic mode functions after carrying out variational mode decomposition on the acquired voltage signals, calculating an envelope Shannon entropy value of each intrinsic mode function, wherein parameters K and alpha corresponding to the minimum envelope Shannon entropy value are optimal parameters of the variational mode decomposition;
and (3): processing the voltage signal by using the optimized variation modal decomposition, calculating a Cramer correlation coefficient between the intrinsic modal component and the original signal, selecting the intrinsic modal component larger than a threshold value, reconstructing the signal and extracting 10-dimensional characteristics of the time domain and the frequency domain of the signal to form a characteristic vector; the 10-dimensional characteristics are respectively the maximum value, the minimum value, the mean value, the square root amplitude, the variance, the standard deviation, the gravity center frequency, the kurtosis, the frequency variance and the mean square frequency of the signal;
and (4): constructing a probabilistic neural network diagnosis model, taking the characteristic vector in the step (3) as the input of the probabilistic neural network, taking the corresponding fault category as the output of the probabilistic neural network, training the probabilistic neural network, and taking the trained model as a power electronic circuit fault diagnosis model;
and (5): and (4) inputting the feature vector acquired in the step (3) into the trained probabilistic neural network model in the step (4) for the voltage signal acquired in the actual power electronic circuit, and obtaining the fault type of the actual power electronic circuit according to the output of the power electronic circuit fault diagnosis model.
2. The method for diagnosing the fault of the power electronic circuit based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm according to claim 1, wherein the voltage signal collected by the simulation model in the step (1) is set as an input voltage signal and an output voltage signal when the nominal value of the electrolytic capacitor in the power electronic circuit is degraded by 2% in sequence.
3. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm according to claim 1, wherein the specific steps of optimizing the variational modal decomposition based on the improved harris eagle optimization algorithm in the step (2) are as follows:
step 3.1: the parameters are set as follows: initializing a population position vector of a Harris eagle to be [ K, alpha ], wherein the population scale is N, the maximum iteration number is T, the upper and lower boundaries are respectively LB and UB, and the dimensionality of an objective function is D;
step 3.2: introducing Circle chaotic mapping to form an initialization population with uniform distribution, and simultaneously recording the current optimal individual and position;
step 3.3: processing the acquired voltage signals by using variational modal decomposition according to the position of each Harris hawk, and calculating the envelope Shannon entropy corresponding to each Harris hawk individual;
step 3.4: updating the escape energy E and the jump strength J, selecting four strategies according to the escape energy E and the escape probability r, and updating the optimal individuals and positions;
step 3.5: performing Cauchy differential variation operation on the current individual, the optimal individual and the randomly selected individual, and calculating and updating the current optimal individual and the position;
step 3.6: and when the constraint condition of the maximum iteration times is met, outputting the optimal parameter combination [ K, alpha ], otherwise, returning to the step 3.3.
4. The power electronic circuit fault diagnosis method for optimizing variable modal decomposition based on the improved harris eagle optimization algorithm according to claim 1, wherein the fitness function of the optimized variable modal decomposition of the harris eagle optimization algorithm in the step (2) is represented as follows:
Fitness＝MESE＝min{IMFESE(1),…,IMFESE(k)}
wherein, the first and the second end of the pipe are connected with each other,
bithe envelope amplitude of the ith modal signal after the variation modal decomposition is obtained, M is the length of the modal signal, piFor normalizing the envelope, IMF, of the modal signalESE(k) Is the shannon entropy value of the envelope of the k-th mode signal.
5. The method for diagnosing faults of a power electronic circuit based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm as claimed in claim 1, wherein the formula of the gram correlation coefficient in the step (3) is as follows:
wherein phi iscThe method is characterized in that the method is a Cramer correlation coefficient, Z is an intrinsic mode component after the variation mode decomposition, Z is an original signal, N is a sample size involved in the test, and N is a small category number of any variable.
6. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm according to claim 1, characterized in that the threshold value set in the step (3) is 0.6.
7. The power electronic circuit fault diagnosis method based on the improved harris eagle optimization algorithm optimized variation modal decomposition of claim 3, wherein the formula of the Ciecle chaotic mapping in the step 3.2 is as follows:
where mod is a remainder function, and a and b are coefficients, taken as 0.6 and 0.3, respectively.
8. The method for diagnosing the fault of the power electronic circuit based on the optimized variational modal decomposition by the improved harris eagle optimization algorithm according to claim 3, wherein the four strategy expressions in the step 3.4 are as follows:
strategy one: when E is more than or equal to 0.5 and r is more than or equal to 0.5, a soft enclosure strategy is adopted, and the formula is as follows:
X(t+1)＝△X(t)-E|JXrabbit(t)-X(t)|
wherein, Δ X (t) ═ Xrabbit(t)-X(t)，J＝2(1-r5)，r5Is [0,1 ]]A random number in between, and a random number,
E0is [ -1,1 [ ]]T is the current iteration number;
and (2) strategy two: when | E | <0.5 and r ≧ 0.5, a hard bounding strategy is adopted, and the formula is as follows:
X(t+1)＝Xrabbit(t)-E|△X(t)|
strategy three: when E is more than or equal to 0.5 and r is less than 0.5, adopting a soft enclosure strategy of gradual quick dive, wherein the formula is as follows:
d is a problem dimension, S is a random vector of the D dimension, and LF is a Levy flight function;
and (4) strategy four: when | E | <0.5 and r <0.5, a hard surrounding strategy of progressive fast dive is adopted, and the formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
Xmand N is the average position of the current population, and is the population quantity.
9. The power electronic circuit fault diagnosis method for optimizing variational modal decomposition based on the improved harris eagle optimization algorithm according to claim 3, characterized in that the mathematical expression of cauchy differential variation in the step 3.5 is as follows:
X(t+1)＝w1·f1·(X*-X(t))+w2·f2·(Xrand-X(t))
wherein, w1And w2Is a weight coefficient; f. of1And f2Taking a standard Cauchy distribution function with a mean value of 0 and a variance of 1 as a coefficient of the Cauchy distribution function; x*For the current optimal individual position, XrandFor a randomly selected position vector of harris hawks, x (t) is the individual position of the current harris hawk.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210300673.3A CN114781435B (en) | 2022-03-24 | 2022-03-24 | Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210300673.3A CN114781435B (en) | 2022-03-24 | 2022-03-24 | Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114781435A true CN114781435A (en) | 2022-07-22 |
| CN114781435B CN114781435B (en) | 2024-05-07 |
Family
ID=82425150
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210300673.3A Active CN114781435B (en) | 2022-03-24 | 2022-03-24 | Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114781435B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703624A (en) * | 2023-05-22 | 2023-09-05 | 淮阴工学院 | Block chain-based enterprise operation diagnosis system and method |
| CN117349623A (en) * | 2023-10-25 | 2024-01-05 | 广西财经学院 | System-level fault diagnosis method based on double-population Harris eagle algorithm |
| CN117349735A (en) * | 2023-12-05 | 2024-01-05 | 国家电投集团云南国际电力投资有限公司 | Fault detection method, device and equipment for direct-current micro-grid and storage medium |
| CN117874434A (en) * | 2024-03-11 | 2024-04-12 | 湖南工程学院 | Data cleaning method in intelligent environmental temperature and humidity detection process |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200285900A1 (en) * | 2019-03-06 | 2020-09-10 | Wuhan University | Power electronic circuit fault diagnosis method based on optimizing deep belief network |
| AU2020104117A4 (en) * | 2020-12-16 | 2021-03-04 | Mohd Aleem Khan | A methodfor fractional order controller in delta domain for electrochemical process |
| CN113240069A (en) * | 2021-05-14 | 2021-08-10 | 江苏科技大学 | RBF neural network optimization method based on improved Harris eagle algorithm |
| CN113361777A (en) * | 2021-06-08 | 2021-09-07 | 淮阴工学院 | Runoff prediction method and system based on VMD decomposition and IHHO optimization LSTM |
-
2022
- 2022-03-24 CN CN202210300673.3A patent/CN114781435B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200285900A1 (en) * | 2019-03-06 | 2020-09-10 | Wuhan University | Power electronic circuit fault diagnosis method based on optimizing deep belief network |
| AU2020104117A4 (en) * | 2020-12-16 | 2021-03-04 | Mohd Aleem Khan | A methodfor fractional order controller in delta domain for electrochemical process |
| CN113240069A (en) * | 2021-05-14 | 2021-08-10 | 江苏科技大学 | RBF neural network optimization method based on improved Harris eagle algorithm |
| CN113361777A (en) * | 2021-06-08 | 2021-09-07 | 淮阴工学院 | Runoff prediction method and system based on VMD decomposition and IHHO optimization LSTM |
Non-Patent Citations (1)
| Title |
|---|
| 郑小霞;陈广宁;任浩翰;***;: "基于改进VMD和深度置信网络的风机易损部件故障预警", 振动与冲击, no. 08, 28 April 2019 (2019-04-28) * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703624A (en) * | 2023-05-22 | 2023-09-05 | 淮阴工学院 | Block chain-based enterprise operation diagnosis system and method |
| CN117349623A (en) * | 2023-10-25 | 2024-01-05 | 广西财经学院 | System-level fault diagnosis method based on double-population Harris eagle algorithm |
| CN117349623B (en) * | 2023-10-25 | 2024-04-19 | 广西财经学院 | System-level fault diagnosis method based on double-population Harris eagle algorithm |
| CN117349735A (en) * | 2023-12-05 | 2024-01-05 | 国家电投集团云南国际电力投资有限公司 | Fault detection method, device and equipment for direct-current micro-grid and storage medium |
| CN117349735B (en) * | 2023-12-05 | 2024-03-26 | 国家电投集团云南国际电力投资有限公司 | Fault detection method, device and equipment for direct-current micro-grid and storage medium |
| CN117874434A (en) * | 2024-03-11 | 2024-04-12 | 湖南工程学院 | Data cleaning method in intelligent environmental temperature and humidity detection process |
| CN117874434B (en) * | 2024-03-11 | 2024-05-24 | 湖南工程学院 | Data cleaning method in intelligent environmental temperature and humidity detection process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114781435B (en) | 2024-05-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114781435B (en) | Power electronic circuit fault diagnosis method based on improved Harris eagle optimization algorithm optimization variation modal decomposition | |
| Yang et al. | Fault diagnosis for energy internet using correlation processing-based convolutional neural networks | |
| CN110175386B (en) | Method for predicting temperature of electrical equipment of transformer substation | |
| CN110738010A (en) | Wind power plant short-term wind speed prediction method integrated with deep learning model | |
| CN106778923B (en) | Electric energy quality disturbance signal classification method and device | |
| Han et al. | Fault diagnosis system of induction motors based on neural network and genetic algorithm using stator current signals | |
| CN107766816A (en) | A kind of Mechanical Failure of HV Circuit Breaker recognition methods based on LVQ neutral nets | |
| CN110826791A (en) | Hybrid wind power prediction method based on long-time and short-time memory neural network | |
| CN111368259B (en) | Distribution network voltage data missing filling method and related device | |
| CN111697621A (en) | Short-term wind power prediction method based on EWT-PDBN combination | |
| CN113659565B (en) | Online prediction method for frequency situation of new energy power system | |
| CN115017787A (en) | Wind power plant voltage ride through characteristic equivalent modeling method and system based on intelligent algorithm | |
| CN114006370B (en) | Power system transient stability analysis and evaluation method and system | |
| CN102034111A (en) | Method for identifying and detecting aircraft structural damage conditions in diversified way | |
| CN114548532A (en) | VMD-based TGCN-GRU ultra-short-term load prediction method and device and electronic equipment | |
| CN115983374A (en) | Cable partial discharge database sample expansion method based on optimized SA-CACGAN | |
| Qiu et al. | Fault diagnosis of analog circuits based on wavelet packet energy entropy and DBN | |
| CN114136619A (en) | Rolling bearing fault diagnosis method under variable working conditions based on convolution self-coding | |
| CN117471252A (en) | DC fault arc diagnosis method based on ICEEMDAN-KPCA feature extraction and SOA-KELM | |
| CN116186586A (en) | Rolling bearing fault diagnosis method based on improved empirical mode decomposition algorithm and optimized deep confidence network | |
| CN115683620A (en) | Gearbox fault diagnosis method based on group decomposition and improved multi-scale weight dispersion entropy | |
| CN113051809A (en) | Virtual health factor construction method based on improved restricted Boltzmann machine | |
| CN111179110B (en) | Virtual power plant variable order aggregation equivalent robust dynamic model modeling method and device | |
| CN114397521A (en) | Fault diagnosis method and system for electronic equipment | |
| Li et al. | Fault diagnosis of analog circuit based on wavelet neural networks and chaos differential evolution algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |